diff --git a/examples/1d_hubbard.ipynb b/examples/1d_hubbard.ipynb
index 79ef0d60d10db5b7ec5377a14ddc702e40e44c31..0fec29d68acd8de67ebb9c3b569ac155adcd3b42 100644
--- a/examples/1d_hubbard.ipynb
+++ b/examples/1d_hubbard.ipynb
@@ -9,10 +9,9 @@
},
"outputs": [],
"source": [
- "import kwant\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
- "from codes import utils, hf, kwant_examples\n",
+ "from codes import utils, hf\n",
"from tqdm import tqdm"
]
},
@@ -43,6 +42,7 @@
"tb_model = {\n",
" (0,) : hopp + hopp.T.conj(),\n",
" (1,) : hopp,\n",
+ " (-1,) : hopp.T.conj()\n",
"}"
]
},
@@ -74,8 +74,12 @@
"source": [
"# Set number of k-points\n",
"nk = 100\n",
+ "hk = utils.model2hk(tb_model=tb_model)\n",
"# Compute Hamiltonian on the corresponding k-point grid\n",
- "hamiltonians_0, ks = utils.kgrid_hamiltonian(nk, tb_model, return_ks=True)\n",
+ "hamiltonians_0, ks = utils.kgrid_hamiltonian(\n",
+ " nk=nk, hk=hk, dim=1,\n",
+ " return_ks=True\n",
+ ")\n",
"# Perform diagonalization\n",
"vals, vecs = np.linalg.eigh(hamiltonians_0)\n",
"# Plot data\n",
@@ -97,34 +101,36 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 4,
"id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
"metadata": {},
"outputs": [],
"source": [
"def compute_gap(\n",
- " tb_model,\n",
- " int_model,\n",
+ " hk,\n",
+ " Vk,\n",
" nk,\n",
" nk_dense,\n",
" filling=2\n",
"):\n",
- " # Generate guess on the same k-point grid\n",
- " guess = utils.generate_guess(nk, tb_model, int_model, scale=1)\n",
- "\n",
" # Find groundstate Hamiltonian on the same grid\n",
- " mf_model = hf.find_groundstate_ham(\n",
- " int_model=int_model,\n",
+ " _, mf_model = hf.find_groundstate_ham(\n",
+ " hk=hk,\n",
+ " Vk=Vk,\n",
" filling=filling,\n",
- " tb_model=tb_model,\n",
- " guess=guess,\n",
+ " dim=1,\n",
" nk=nk,\n",
- " tol=1e-8\n",
+ " cutoff=1,\n",
+ " return_model=True\n",
" )\n",
" # Generate Hamiltonian on a denser k-point grid\n",
- " hk = utils.kgrid_hamiltonian(nk_dense, mf_model)\n",
+ " mf_k = utils.kgrid_hamiltonian(\n",
+ " nk=nk_dense,\n",
+ " hk=utils.model2hk(tb_model=mf_model),\n",
+ " dim=1\n",
+ " )\n",
" # Diagonalize groundstate Hamiltonian\n",
- " vals, vecs = np.linalg.eigh(hk)\n",
+ " vals, _ = np.linalg.eigh(mf_k)\n",
" # Extract dense-grid Fermi energy\n",
" E_F = utils.get_fermi_energy(vals, filling)\n",
" gap = utils.calc_gap(vals, E_F)\n",
@@ -150,7 +156,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 5,
"id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
"metadata": {
"tags": []
@@ -169,9 +175,10 @@
" int_model = {\n",
" (0,) : U * np.kron(np.ones((2, 2)), np.eye(2))\n",
" }\n",
+ " Vk = utils.model2hk(tb_model=int_model)\n",
" _gap, _vals = compute_gap(\n",
- " tb_model=tb_model,\n",
- " int_model=int_model,\n",
+ " hk=hk,\n",
+ " Vk=Vk,\n",
" nk=nk,\n",
" nk_dense=nk_dense,\n",
" )\n",
@@ -182,7 +189,7 @@
},
{
"cell_type": "code",
- "execution_count": 126,
+ "execution_count": 6,
"id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
"metadata": {
"tags": []
@@ -192,7 +199,14 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 20/20 [00:31<00:00, 1.60s/it]\n"
+ " 0%| | 0/20 [00:00<?, ?it/s]"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 20/20 [00:12<00:00, 1.63it/s]\n"
]
}
],
@@ -205,7 +219,7 @@
},
{
"cell_type": "code",
- "execution_count": 127,
+ "execution_count": 7,
"id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
"metadata": {},
"outputs": [],
@@ -231,7 +245,7 @@
},
{
"cell_type": "code",
- "execution_count": 128,
+ "execution_count": 8,
"id": "868cf368-45a0-465e-b042-6182ff8b6998",
"metadata": {},
"outputs": [
@@ -270,7 +284,7 @@
},
{
"cell_type": "code",
- "execution_count": 129,
+ "execution_count": 9,
"id": "ac2eb725-f3bd-4d5b-a509-85d0d0071958",
"metadata": {},
"outputs": [
@@ -301,17 +315,17 @@
},
{
"cell_type": "code",
- "execution_count": 131,
+ "execution_count": 10,
"id": "5499ea62-cf1b-4a13-8191-ebb73ea38704",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array(1.00352528)"
+ "array(1.00352529)"
]
},
- "execution_count": 131,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -322,7 +336,7 @@
},
{
"cell_type": "code",
- "execution_count": 132,
+ "execution_count": 11,
"id": "0cb395cd-84d1-49b4-89dd-da7a2d09c8d0",
"metadata": {},
"outputs": [],
@@ -347,7 +361,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.11.6"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
diff --git a/examples/data/1d_hubbard_example.nc b/examples/data/1d_hubbard_example.nc
index 7ea1d4f36266ed22a7b8a12472c15f3f9b41c02b..c973f6b2aaaf2a6d41d0438192854bac87ec2b2b 100644
Binary files a/examples/data/1d_hubbard_example.nc and b/examples/data/1d_hubbard_example.nc differ